Survival in MS
Bibliographic record
Abstract
OBJECTIVE: To examine the effects of interferon beta (IFNβ)-1b on all-cause mortality over 21 years in the cohort of 372 patients who participated in the pivotal randomized clinical trial (RCT), retaining (in the analysis) the original randomized treatment-assignments. METHODS: For this randomized long-term cohort study, the primary outcome, defined before data collection, was the comparison of all-cause mortality between the IFNβ-1b 250 μg and placebo groups from the time of randomization through the entire 21-year follow-up interval (intention-to-treat, log-rank test for Kaplan-Meier survival curves). All other survival outcomes were secondary. RESULTS: After a median of 21.1 years from RCT enrollment, 98.4%(366 of 372) of patients were identified, and, of these, 81 deaths were recorded (22.1% [81 of 366]). Patients originally randomly assigned to IFNβ-1b 250 μg showed a significant reduction in all-cause mortality over the 21-year period compared with placebo (p = 0.0173), with a hazard ratio of 0.532 (95% confidence interval 0.314-0.902). The hazard rate of death at long-term follow-up by Kaplan-Meier estimates was reduced by 46.8% among IFNβ-1b 250 μg-treated patients (46.0% among IFNβ-1b 50 μg-treated patients) compared with placebo. Baseline variables did not influence the observed treatment effect. CONCLUSIONS: There was a significant survival advantage in this cohort of patients receiving early IFNβ-1b treatment at either dose compared with placebo. Near-complete ascertainment, together with confirmatory findings from both active treatment groups, strengthens the evidence for an IFNβ-1b benefit on all-cause mortality. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that early treatment with IFNβ-1b is associated with prolonged survival in initially treatment-naive patients with relapsing-remitting multiple sclerosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".